Communication Dans Un Congrès Année : 2019

A novel method to generalize time-frequency coherence analysis between EEG or EMG signals during repetitive trials with high intra-subject variability in duration

Résumé

Time-frequency coherence analysis between EEG and EMG signals represents a valuable tool to gain insight into neural mechanisms underlying motor control. However, for self-paced movements, the variability of inter-trial duration limits its proper use. To overcome this obstacle, we propose a time-normalizing approach and test it on both simulated and experimental data recorded during elbow extension movements performed by a post-stroke subject. Results show that the proposed time-normalization improves both the consistency and the accuracy of time-frequency coherence calculation, detection and quantification. The proposed time-normalization overcomes a major limitation to generalization of coherence analysis and can be suggested as an essential step to perform for coherence in presence of high intra-subject variability in duration.

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Dates et versions

hal-04364689 , version 1 (27-12-2023)

Identifiants

Citer

Maxime Fauvet, Sylvain Cremoux, Alexandre Chalard, Joseph Tisseyre, David Gasq, et al.. A novel method to generalize time-frequency coherence analysis between EEG or EMG signals during repetitive trials with high intra-subject variability in duration. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), Mar 2019, San Francisco (CA), United States. pp.437-440, ⟨10.1109/NER.2019.8716973⟩. ⟨hal-04364689⟩
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